np.select 将所有真实值连接在一起

Question

我有一个数据框，想检查每一行我的条件是否正确。如果 multiple 为真，我想用 np.select 返回所有这些选择。我该怎么做？

df = pd.DataFrame({'cond1':[True, True, False, True],
                   'cond2':[False, False, True, True],
                   'cond3':[True, False, False, True],
                   'value': [1, 3, 3, 6]})

conditions = [df['cond1'] & (df['value']>4),
             df['cond2'],
             df['cond2'] & (df['value']>2),
             df['cond3'] & df['cond2']]

choices     = [ '1', '2', '3', '4']

df["class"] = np.select(conditions, choices, default=np.nan)

我明白了

   cond1    cond2   cond3   value   class
0   True    False   True    1       nan
1   True    False   False   3       nan
2   False   True    False   3       2
3   True    True    True    6       1

但想得到这个

   cond1    cond2   cond3   value   class
0   True    False   True    1       nan
1   True    False   False   3       nan
2   False   True    False   3       2 and 3
3   True    True    True    6       1 and 2 and 3 and 4

Answer 1

在此示例中，我创建了一个 check_conditions 函数，该函数单独检查每一行的每个条件，并将满足条件的标签附加到列表中。然后，它用“and”连接这些标签，以表示“class”列中的组合条件。如果不满足任何条件，则分配 NaN。

这有帮助吗？

import pandas as pd
import numpy as np

# Creating the DataFrame
df = pd.DataFrame({'cond1': [True, True, False, True],
                   'cond2': [False, False, True, True],
                   'cond3': [True, False, False, True],
                   'value': [1, 3, 3, 6]})

# Define a function to check conditions for each row
def check_conditions(row):
    # your conditions
    conditions = [
        (row['cond1'] and (row['value'] > 4)),
        row['cond2'],
        (row['cond2'] and (row['value'] > 2)),
        (row['cond3'] and row['cond2'])
    ]
    
    # your condition labels
    choices = ['1', '2', '3', '4']
    
    # Check which conditions are met for the current row and store the corresponding choices
    conditions_met = [str(choice) for condition, choice in zip(conditions, choices) if condition]
    
    # Join the labels of met conditions using 'and', or assign NaN if no conditions are met
    return ' and '.join(conditions_met) if conditions_met else np.nan

# Apply the check_conditions function to each row of the DataFrame
df['class'] = df.apply(check_conditions, axis=1)